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Leveraging Evolutionary Tuning for Enhanced Building Model Parameter Optimization

This study discusses an evolutionary approach to optimizing building model parameters, resulting in significant reductions in monthly electrical usage by nearly 20% (250 kWh) and hourly consumption by over 10% (700 kWh). The methodology employs various evolutionary computation techniques including genetic algorithms and particle swarm optimization. It simulates natural selection to fine-tune 108 real-valued parameters, enhancing model accuracy by minimizing errors between simulated outputs and actual sensor data. Future work aims to integrate machine learning and refine fitness functions for further optimization.

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Leveraging Evolutionary Tuning for Enhanced Building Model Parameter Optimization

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  1. Evolutionary Tuning of Building Model Parameters Aaron Garrett Jacksonville State University

  2. Conclusion Evolutionary approach reduces electrical… • monthly SAE by almost 20% (250 kWh) • hourly SAE by over 10% (700 kWh) • hourly RMSE by over 7%

  3. Evolution is a search algorithm • Type of beam search • Less vulnerable to local optima • Optimizes based on environment

  4. Evolutionary computation • Simulates evolution by natural selection • Genetic algorithms • Evolution strategies • Genetic programs • Particle swarm optimization • Ant colony optimization • Problem domain information is invaluable

  5. An evolutionary approach • Individual: Building parameters • Fitness: Error between E+ output and sensor data

  6. What is an individual? • Defined by 108 real-valued parameters • Material • Thickness • Conductivity • Density • Specific Heat • Thermal Absorptance • Solar Absorptance • Visible Absorptance • WindowMaterial:SimpleGlazingSystem • U-Factor • Solar Heat • ZoneInfiltration:FlowCoefficient • Shadow Calculation Frequency

  7. What is the fitness? Individual Model Error Fitness Actual Building Data

  8. How do they evolve? Mom Sister Dad Brother

  9. How are offspring produced? • Average each component • Add Gaussian noise

  10. EC parameters • Population size 16 • Tournament selection (tournament size 4) • Generational replacement with weak elitism (1 elite) • Gaussian mutation (mutation rate 10% of variable range) • Heuristic crossover

  11. Building model search space • 108 dimensions • Effectively infinite because continuous-valued • Limit here is 1024 simulations per search • Approximately what could be done in a weekend on single-core processor • 1024 is incredibly small number of samples

  12. How do we get more for less? • EnergyPlus is slow • Full-year schedule • 8 – 10 minutes per simulation • Use abbreviated 4-day schedule instead • Jan 1, Apr 1, Aug 1, Nov 1 • 15 – 30 seconds per simulation

  13. Will that even work? • 4 independent random trials • 1024 simulations per trial • Samples taken from high to low error r = 0.96 r = 0.94 Monthly Electrical Usage Hourly Electrical Usage

  14. The less expensive approach Individual Model Error Fitness Actual Building Data

  15. About that actual data… • 2% of the 15-minute measurements failed • Monthly electrical usage • Just ignore missing data (treat as 0) • Hourly electrical usage • Any hour containing a single failure was counted as a failure (8%) • Failures were not counted in error measure

  16. How good are the existing models?

  17. Evolve using 4-day schedule • 8 independent trials • 1024 simulations per trial 15% 13% 9% 8% 6% 7% 26% 35% 60% Monthly SAE Hourly SAE Hourly RMSE

  18. And the full year schedule? • Only run on hourly usage • 8 independent trials • 1024 simulations per trial 9% 11% 8% 12% 6% 7% 7% 10% Hourly SAE Hourly RMSE

  19. Combining the two… Evolve Evolve

  20. Serial evolution • 8 independent trials • 1024 simulations per trial • 768 simulations for abbreviated; 256 simulations for full 11% 11% 12% 9% 7% 7% 10% 8% Hourly SAE Hourly RMSE

  21. Combining a different way… On-deck Circle

  22. Parallel evolution • 8 independent trials • 256 simulations for full year schedule • 768 simulations for abbreviated schedule 11% 10% 9% 10% 7% 7% 8% 9% Hourly SAE Hourly RMSE

  23. A bit surprising… 25%

  24. Conclusion Evolutionary approach reduces electrical… • monthly SAE by almost 20% (250 kWh) • hourly SAE by over 10% (700 kWh) • hourly RMSE by over 7%

  25. What’s next? • Incorporate machine learning as fast island • Include temperature errors in fitness • How should this be combined with electrical usage error? • Should the be optimized separately with EMO approach?

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